A0671
Title: Embedding distributional data
Authors: Ery Arias-Castro - UC San Diego (United States)
Wanli Qiao - George Mason University (United States) [presenting]
Abstract: The purpose is to adapt concepts, methodology, and theory originally developed in the areas of multidimensional scaling and dimensionality reduction for Euclidean data to be applicable to distributional data. The focus is on classical scaling and Isomap, prototypical methods that have played important roles in these areas, and showcase their use in the context of distributional data analysis. In the process, the crucial role that the ambient metric plays is highlighted.
